Tableau-CRM-and-Einstein-Discovery-Consultant Exam Dumps - Salesforce Tableau CRM Einstein Discovery Consultant (SU24)

The scenario described where Yasmine cannot see any rows in the Seed Bank Orders dataset while Hadiqa can, likely indicates that the dataset's security predicate was set up to restrict data visibility based on user roles or attributes. Security predicates in Salesforce Tableau CRM are used to implement row-level security, ensuring that users only see data pertinent to their role or permissions within the organization. This security setup is crucial for maintaining data privacy and compliance. Reference: Salesforce Help - Row-Level Security

Before releasing an updated dashboard to production, the best practice is to remove redundant steps to ensure dashboard performance. This optimization helps in minimizing the load time and resource consumption, particularly important after iterative modifications such as adding or removing charts.

To meet the security requirements of allowing only a subset of users to access the dataset and ensuring they only see data relevant to their region, the consultant can:

• Use a security predicate in the dataset so users can see only rows belonging to their respective regions (B): This involves configuring row-level security using security predicates based on the UserRegion field, which filters data dynamically based on the user's region.
• Save the dataset under a new application and give "viewer" access to a public group containing the list of users allowed to see the dataset (D): This method segregates access at the application level, allowing only designated users to view the dataset within that application.

In a scenario where Recipe3 depends on Dataset1 created by Recipe1, while Recipe1 and Recipe2 have no dependencies, the optimal scheduling strategy is to make Recipe1 and Recipe2 time-based and Recipe3 event-based, triggered by the successful completion of Recipe1. This ensures that Recipe3 only processes data after Dataset1 is updated and available, maintaining data integrity and workflow efficiency.

Outliers in a dataset can significantly impact the results of a machine learning model, including those built by Einstein Discovery.expand_more Here's why changing binning methods is often the best approach:

• Outliers and Machine Learning: Outliers are extreme values that fall far outside the typical range of data.expand_more They can distort statistical summaries and skew the model's understanding of relationships within the data.
• Binning: Binning is the process of dividing continuous numerical data into discrete groups or bins.expand_more In Einstein Discovery, binning affects how data is visualized and analyzed.
• Fixed-width Binning: With fixed-width binning, you define equal-sized intervals for grouping data points. This can help mitigate the impact of outliers by spreading their influence across a wider range, reducing their effect on individual bins and the overall model.

Why other options are less ideal:

• A. Remove the outlier rows: While removing outliers can be effective, it risks discarding potentially valuable information. It's best used only when outliers are definitively incorrect.
• B. Nothing, because the field is not the outcome variable: Even predictor variables (inputs) with outliers can negatively impact the model. Outliers distort relationships the model is trying to learn.expand_more
• D. Remove the field because it has bad data: This is too drastic unless the whole field is unreliable. Binning adjustments are often enough to address outlier issues.

The Actions menu in a chart within Salesforce Tableau CRM (Einstein Analytics) typically includes options that enhance interactivity and integration with Salesforce features. One of the key functionalities offered by the Actions menu is the ability to post to Chatter directly from a chart. This allows users to share insights, collaborate on data visualizations, and engage with team members directly within the flow of analytics. Reference: Salesforce Help - Chatter in Dashboards

Regarding Einstein Analytics Encryption, the true statements are:

• A: When Einstein Analytics Encryption is enabled, all newly registered datasets are encrypted at rest. This ensures that any data stored within the platform is secured against unauthorized access, protecting sensitive information.
• C: The customer's org must have a Shield Platform Encryption tenant secret. This requirement is necessary for enabling encryption capabilities within Salesforce, which includes Einstein Analytics, ensuring data security complies with organizational and regulatory standards.